DLTK documentation¶

DLTK is a neural networks toolkit written in python, on top of Tensorflow. Its modular architecture is closely inspired by sonnet and it was developed to enable fast prototyping and ensure reproducibility in image analysis applications, with a particular focus on medical imaging. Its goal is to provide the community with state of the art methods and models and to accelerate research in this exciting field.

Road map¶

Over the course of the next months we will add more content to DLTK. This road map outlines the immediate plans for what you will be seeing in DLTK soon:

Medical model zoo: Pre-trained models on medical images and deploy scripts
Core: Losses: Dice loss, frequency reweighted losses, adversial training Normalisation: layer norm, weight norm
Network architectures: deepmedic, densenet, VGG, super-resolution networks
Other: Augmentation via elastic deformations Sampling with fixed class frequencies